Project Objectives

Background

The legal industry faces an ever-increasing volume of documents, contracts, and case law. This abundance of information presents significant challenges for legal professionals in efficiently researching, analyzing, and extracting key information. Traditional methods of legal research are time-consuming and often struggle to capture the full nuances of court cases, especially in complex areas such as employment discrimination law.

Problem Statement

Legal professionals struggle with the time-consuming and complex task of identifying relevant precedents and predicting case outcomes based on historical decisions. This can lead to inefficiencies, increased costs, and potentially inconsistent legal interpretations.

Project Scope

This project focuses on developing an AI-powered system for enhancing legal research and case law analysis, with a specific emphasis on employment discrimination cases under Title VII of the Civil Rights Act of 1964. The system incorporates advanced natural language processing techniques, including a novel Hybrid RAG approach that combines vector-based and graph-based retrieval methods.

The primary objectives of this project are:

• Create a generative AI model capable of accurately summarizing legal documents.
• Develop a user-friendly interface that allows legal professionals to efficiently interact with the summarization tool.
• Evaluate the system's performance and iteratively improve it based on user feedback.
• Deploy the solution and ensure its long-term maintenance and reliability.